Abstract:

The aim of this thesis is to evaluate the viability of transposition as a technique for generating new data that can be used to improve the accuracy of a chord recognition system. Transposition is the process of shifting all the notes in a piece by a fixed interval (i.e. changing the key of the piece). Any piece of music can be transposed 11 times before returning back to its original key. We took each piece in our dataset and created 11 different versions of each piece which effectively expanded our dataset by 12 times. This is a potential solution to the perennial problem in chord recognition: the lack of training data. It is a well-known fact a machine learning model needs large volumes of training data but labeled chord data is scarce. We want to see if transposition can help remedy this situation by providing a convenient way of creating more data.To test the effectiveness of this technique, we trained three different Hidden Markov models on different quantities of transformed data: a baseline model that contained no transformed data, an experimental model that contained the original tracks plus 5 transposed versions of each track, and an experimental model that contained the original tracks plus 11 transposed versions of each track. We achieved a recognition accuracy of 33.3% for our baseline model. We can say tentatively that transposition is not a viable technique for generating data.